The present invention relates to a visual system, and more particularly to a method of and an apparatus for forming a passive three-dimensional stereo vision which is capable of extracting three-dimensional distance information.
The above method and apparatus are applicable to various fields requiring an accurate, high-speed passive visual system such as an automobile (for the purpose of guiding the automobile when the automobile is driven into a garage, is parked, or runs on a snarl-up road or a highway, and for the purpose of facilitating the navigation of the automobile on an ordinary road), vehicles other than the automobile, a robot, factory automation, laboratory automation, office automation, building automation, home automation, and precise measurement.
In other words, the method and apparatus are used in operatorless repairs, an inspecting operation, an operatorless wagon, and operatorless crane, operatorless construction, civil engineering machinery, the assembly of parts, measurement of the number of queuing persons, a burglar-proof system, a disaster prevention system, a blindman guiding system, a speed detector, a distance detector, an object detector, an automatic focusing mechanism for each of a microscope, an enlarger, a projector, a copying machine, an optical disc apparatus, an image pickup device, a camera and others, character/figure recognition, the recognition of a number plate, a stereoscopic camera (for a still or moving object) and a game/leisure machine.
In a conventional method of forming a three-dimensional stereo vision, two brightness data obtained by two eyes are caused to correspond to each other (that is, corresponding points of the two brightness data are determined) by pattern matching techniques. What is meant by pattern matching techniques is that the scale (namely, measure) of a coordinate space is enlarged or contracted at each of the points in that portion of the coordinate space which has brightness distribution, so that the difference between the brightness distribution in the coordinate space whose scale has been changed and reference brightness distribution becomes minimum under some criterion.
Such pattern matching techniques encounter with two problems, the first one of which is as follows. The number of coordinate points where the scale of the coordinate space is to be enlarged or contracted, is equal to the number of bright points (herein referred to as "bright lines") formed on a sensing surface, and the position of each of the coordinate points can be freely changed, provided that the configurational order of the bright lines is not changed. Accordingly, a vast number of combinations of scale transformation are basically allowed, and a combination capable of minimizing the difference between the brightness distribution obtained after scale transformation and the reference brightness distribution, is selected from a multiplicity of combinations. Thus, it takes a lot of time to carry out pattern matching by digital processing.
The second problem of the pattern matching techniques is as follows. As mentioned above, the scale transformation is made so that the configurational order of the bright lines is not changed. In a case where the brightness distribution at an object system is such that a first group of bright lines which is formed on a sensing surface by the first image focusing lens system, is different in the configurational order of bright lines from the second group of bright lines which is formed on the sensing surface by the second image focusing lens system, pattern patching is attended with a fundamental error. This error is attended for an object system in which the spacing between two object points in the direction of depth is greater than the spacing between the object points in a direction corresponding to the change of longitudinal azimuth angle. It is to be noted that, when the object points and the image focusing lens systems are in the same plane surface, the direction from the image focusing lens systems towards the object points in the above plane surface is herein referred to as "direction of depth", and an angle between the straight line connecting the image focusing lens systems and the straight line connecting one of the image focusing lens systems with one of the object points is herein referred to as a "horizontal azimuth angle". Incidentally, an angle between the above plane surface and the image sensing surface is herein referred to as the "vertical azimuth angle", which will be mentioned later.
In a case where an object system is not formed of independent object points but has continuous spatial distribution, the continuous distribution is converted into discrete distribution to make it possible to use digital processing. Accordingly, this case also encounters the above-mentioned two problems.
Further, even when a large number of brightness distribution data are used for increasing the accuracy of pattern matching, these two problems are unavoidable, provided that the above-mentioned pattern matching techniques are used.